Method TestBoostFromAvgParam

tests/cpp/test_learner.cc:502–539

  }

  void TestBoostFromAvgParam() {
    std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
    learner->SetParam("objective", "reg:absoluteerror");
    learner->SetParam("base_score", "1.3");
    Json config(Object{});
    learner->Configure();
    learner->SaveConfig(&config);

    auto base_score = GetBaseScore(config);
    ASSERT_EQ(base_score.size(), 1);
    // no change
    ASSERT_FLOAT_EQ(base_score[0], 1.3);

    HostDeviceVector<float> predt;
    learner->Predict(Xy_, false, &predt, 0, 0);
    auto h_predt = predt.ConstHostSpan();
    for (auto v : h_predt) {
      ASSERT_FLOAT_EQ(v, 1.3);
    }
    learner->UpdateOneIter(0, Xy_);
    learner->SaveConfig(&config);
    base_score = GetBaseScore(config);
    ASSERT_EQ(base_score.size(), 1);
    // no change
    ASSERT_FLOAT_EQ(base_score[0], 1.3);

    auto from_avg = std::stoi(
        get<String const>(config["learner"]["learner_model_param"]["boost_from_average"]));
    // from_avg is disabled when base score is set
    ASSERT_EQ(from_avg, 0);
    // in the future when we can deprecate the binary model, user can set the parameter directly.
    learner->SetParam("boost_from_average", "1");
    learner->Configure();
    learner->SaveConfig(&config);
    from_avg = std::stoi(
        get<String const>(config["learner"]["learner_model_param"]["boost_from_average"]));
    ASSERT_EQ(from_avg, 1);
  }

  void TestInitAfterLoad() {
    std::unique_ptr<Learner> learner{Learner::Create({Xy_})};